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Comparing Apples with Oranges: Evaluating Twelve Paradigms of Agency

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Programming Multi-Agent Systems (ProMAS 2006)

Abstract

We report on a study in which twelve different paradigms were used to implement agents acting in an environment which borrows elements from artificial life and multi-player strategy games. In choosing the paradigms we strived to maintain a balance between high level, logic based approaches to low level, physics oriented models; between imperative programming, declarative approaches and “learning from basics” as well as between anthropomorphic or biologically inspired models on one hand and pragmatic, performance oriented approaches on the other.

Instead of strictly numerical comparisons (which can be applied to certain pairs of paradigms, but might be meaningless for others), we had chosen to view each paradigm as a methodology, and compare the design, development and debugging process of implementing the agents in the given paradigm.

We found that software engineering techniques could be easily applied to some approaches, while they appeared basically meaningless for other ones. The performance of some agents were easy to predict from the start of the development, for other ones, impossible. The effort required to achieve certain functionality varied widely between the different paradigms. Although far from providing a definitive verdict on the benefits of the different paradigms, our study provided a good insight into what type of conceptual, technical or organizational problems would a development team face depending on their choice of agent paradigm.

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References

  1. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)

    Google Scholar 

  2. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic programming - an introduction: On the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers, San Francisco (1998)

    MATH  Google Scholar 

  3. Bölöni, L., Turgut, D.: YAES - a modular simulator for mobile networks. In: 8-th ACM/IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems MSWIM 2005, IEEE Computer Society Press, Los Alamitos (2005)

    Google Scholar 

  4. Bouvier, E., Cohen, E., Najman, L.: From crowd simulation to airbag deployment: Particle systems, a new paradigm of simulation. J. Electronic Imaging 6(1), 94–107 (1997)

    Article  Google Scholar 

  5. Gonzalez, A.J., Ahlers, R.H.: Context-based representation of intelligent behavior in simulated opponents. In: Proceedings of the Computer Generated Forces and Behavior Representation Conference (1996)

    Google Scholar 

  6. Hanks, S., Pollack, M.E., Cohen, P.R.: Benchmarks, testbeds, controlled experimentation, and the design of agent architectures. AI Magazine 14(4), 17–42 (1993)

    Google Scholar 

  7. Hodjat and Shahrzad. Introducing a dynamic problem solving scheme based on a learning algorithm in artificial life environemtns. In: IEEE International Conference on Neural Networks. IEEE World Congress on Computational Intelligence, pp. 2333–2338 (1994)

    Google Scholar 

  8. Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  9. Neumann, J.V., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1944)

    MATH  Google Scholar 

  10. Koza, J.R.: Genetically breeding populations of computer programs to solve problems in artificial intelligence. In: Proceedings of the Second International Conference on Tools for AI, Herndon, Virginia, USA, 6-9 Nov. 1990, pp. 819–827. IEEE Computer Society Press, Los Alamitos (1990)

    Google Scholar 

  11. Likhachev, M., Kaess, M., Kira, Z., Arkin, R.C.: Spatio-temporal case-based reasoning for efficient reactive robot navigation (2005)

    Google Scholar 

  12. McCabe: A complexity measure. IEEE Transactions on Software Engineering 2, 308–320 (1976)

    Google Scholar 

  13. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  14. Reif, J., Wang, H.: Social potential fields: A distributed behavioral control for autonomous robots. In: Proceedings of the International Workshop on Algorithmic Foundations of Robotics (WAFR), pp. 431–459 (1995)

    Google Scholar 

  15. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations, pp. 318–362. MIT Press, Cambridge (1986)

    Google Scholar 

  16. Schank, R.C.: Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge University Press, New York (1983)

    Google Scholar 

  17. Scheutz, M.: Useful roles of emotions in artificial agents: A case study from artificial life. In: McGuinness, D.L., Ferguson, G. (eds.) AAAI, pp. 42–48. AAAI Press, Menlo Park (2004)

    Google Scholar 

  18. Yaeger, L.: Computational genetics, physiology, metabolism, neural systems, learning, vision and behavior or PolyWorld: Life in a new context. In: Langton, C.G. (ed.) Artificial Life III, Proceedings Volume XVII, pp. 263–298. Addison-Wesley, Reading (1994)

    Google Scholar 

  19. Yannakakis, G.N., Levine, J., Hallam, J., Papageorgiou, M.: Performance, robustness and effort cost comparison of machine learning mechanisms in FlatLand. In: IEEE Proceedings of the 11th Mediterranean Conference on Control and Automation, June 2003, IEEE Computer Society Press, Los Alamitos (2003)

    Google Scholar 

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Rafael H. Bordini Mehdi Dastani Jürgen Dix Amal El Fallah Seghrouchni

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Luotsinen, L.J. et al. (2007). Comparing Apples with Oranges: Evaluating Twelve Paradigms of Agency. In: Bordini, R.H., Dastani, M., Dix, J., Seghrouchni, A.E.F. (eds) Programming Multi-Agent Systems. ProMAS 2006. Lecture Notes in Computer Science(), vol 4411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71956-4_6

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  • DOI: https://doi.org/10.1007/978-3-540-71956-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71955-7

  • Online ISBN: 978-3-540-71956-4

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